Dataset Information

A Systematic Review of Shared Decision-Making Interventions for Service Users With Serious Mental Illnesses: State of the Science and Future Directions.

ABSTRACT:

Objective

Shared decision making (SDM) is a health communication model that may be particularly appealing to service users with serious mental illnesses, who often want to be involved in making decisions about their mental health care. The purpose of this systematic review was to describe and evaluate participant, intervention, methodological, and outcome characteristics of SDM intervention studies conducted within this population.

Methods

Systematic searches of the literature through April 2020 were conducted and supplemented by hand searching of reference lists of identified studies. A total of 53 independent studies of SDM interventions that were conducted with service users with serious mental illnesses and that included a quantitative or qualitative measure of the intervention were included in the review. Data were independently extracted by at least two authors.

Results

Most studies were conducted with middle-age, male, White individuals from Western countries. Interventions fell into the following categories: decision support tools only, multicomponent interventions involving decision support tools, multicomponent interventions not involving decision support tools, and shared care planning and preference elicitation interventions. Most studies were randomized controlled trials with sufficient sample sizes. Outcomes assessed were diverse, spanning decision-making constructs, clinical and functional, treatment engagement or adherence, and other constructs.

Conclusions

Findings suggest important future directions for research, including the need to evaluate the impact of SDM in special populations (e.g., young adults and racial-ethnic minority groups); to expand interventions to a broader array of decisions, users, and contexts; and to establish consensus measures to assess intervention effectiveness.

SUBMITTER: Thomas EC

PROVIDER: S-EPMC8570969 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

A Systematic Review of Shared Decision-Making Interventions for Service Users With Serious Mental Illnesses: State of the Science and Future Directions.

Thomas Elizabeth C EC Ben-David Shelly S Treichler Emily E Roth Stephanie S Dixon Lisa B LB Salzer Mark M Zisman-Ilani Yaara Y

Psychiatric services (Washington, D.C.) 20210809 11

<h4>Objective</h4>Shared decision making (SDM) is a health communication model that may be particularly appealing to service users with serious mental illnesses, who often want to be involved in making decisions about their mental health care. The purpose of this systematic review was to describe and evaluate participant, intervention, methodological, and outcome characteristics of SDM intervention studies conducted within this population.<h4>Methods</h4>Systematic searches of the literature thr ...[more]

PMID: 34369801

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Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

Project description:BackgroundShared decision making (SDM) is important in achieving patient-centered care. SDM tools such as decision aids are intended to inform the patient. When used to assist in decision making between treatments, decision aids have been shown to reduce decisional conflict, increase ease of decision making, and increase modification of previous decisions.ObjectiveThe purpose of this systematic review is to assess the impact of computerized decision aids on patient-centered outcomes related to SDM for seriously ill patients.MethodsPubMed and Scopus databases were searched to identify randomized controlled trials (RCTs) that assessed the impact of computerized decision aids on patient-centered outcomes and SDM in serious illness. Six RCTs were identified and data were extracted on study population, design, and results. Risk of bias was assessed by a modified Cochrane Risk of Bias Tool for Quality Assessment of Randomized Controlled Trials.ResultsSix RCTs tested decision tools in varying serious illnesses. Three studies compared different computerized decision aids against each other and a control. All but one study demonstrated improvement in at least one patient-centered outcome. Computerized decision tools may reduce unnecessary treatment in patients with low disease severity in comparison with informational pamphlets. Additionally, electronic health record (EHR) portals may provide the opportunity to manage care from the home for individuals affected by illness. The quality of decision aids is of great importance. Furthermore, satisfaction with the use of tools is associated with increased patient satisfaction and reduced decisional conflict. Finally, patients may benefit from computerized decision tools without the need for increased physician involvement.ConclusionsMost computerized decision aids improved at least one patient-centered outcome. All RCTs identified were at a High Risk of Bias or Unclear Risk of Bias. Effort should be made to improve the quality of RCTs testing SDM aids in serious illness.

Dataset Information

A Systematic Review of Shared Decision-Making Interventions for Service Users With Serious Mental Illnesses: State of the Science and Future Directions.

Objective

Methods

Results

Conclusions

Publications

A Systematic Review of Shared Decision-Making Interventions for Service Users With Serious Mental Illnesses: State of the Science and Future Directions.

Similar Datasets

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